Terrain Classification and Classifier Fusion for Planetary Exploration Rovers

• Knowledge of the physical properties of terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Two classification algorithms for color, texture, and range features are presented based on maximum likelihood estimation and support vector machines. In addition, a classification method based on vibration features derived from rover wheel-terrain interaction is briefly described. Two techniques for merging the results of these "low-level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performance of these algorithms is studied using images from NASA's Mars Exploration Rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual and tactile features.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

Nyckelord

Bayesian methods

Classification algorithms

Layout

Mars

Maximum likelihood estimation

Merging

Support vector machine classification

Support vector machines

Testing

Wheels

Bayes methods

Image classification

Image colour analysis

Image texture

Maximum likelihood estimation

Planetary rovers

Sensor fusion

Support vector machines

Bayesian fusion

Mars

NASA Mars Exploration Rover mission

Classifier fusion

Four-wheeled test-bed rover

Maximum likelihood estimation

Meta-classifier fusion

Multi-sensor terrain classification

Planetary exploration rovers

Rover wheel-terrain interaction

Support vector machines

Vibration features

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)